From 4a4027aa558e95a064aa928b3b05605a0a0b4428 Mon Sep 17 00:00:00 2001
From: Paul Andrey <paul.andrey@inria.fr>
Date: Mon, 20 Jan 2025 15:51:08 +0100
Subject: [PATCH] Fix possible issues with numpy 2 and future torch versions.
---
declearn/metrics/_classif.py | 2 +-
declearn/metrics/_roc_auc.py | 4 ++--
declearn/model/sklearn/_sgd.py | 2 +-
declearn/model/torch/_model.py | 4 ++--
4 files changed, 6 insertions(+), 6 deletions(-)
diff --git a/declearn/metrics/_classif.py b/declearn/metrics/_classif.py
index 5486d774..c50e31f9 100644
--- a/declearn/metrics/_classif.py
+++ b/declearn/metrics/_classif.py
@@ -219,7 +219,7 @@ class MulticlassAccuracyPrecisionRecall(Metric[ClassifConfmat]):
"f-score": 2 * diag / (pred + true),
}
# Convert NaNs resulting from zero-division to zero.
- scores = {k: np.nan_to_num(v, copy=False) for k, v in scores.items()}
+ scores = {k: np.nan_to_num(v) for k, v in scores.items()}
# Add a copy of the confusion matrix and return.
scores["confusion"] = confmat.copy()
return scores
diff --git a/declearn/metrics/_roc_auc.py b/declearn/metrics/_roc_auc.py
index 65cb8cf3..f5bfb986 100644
--- a/declearn/metrics/_roc_auc.py
+++ b/declearn/metrics/_roc_auc.py
@@ -291,8 +291,8 @@ class BinaryRocAUC(Metric[AurocState]):
fneg = self._states.fneg[::-1]
# Compute true- and false-positive rates and derive AUC.
with np.errstate(invalid="ignore"):
- tpr = np.nan_to_num(tpos / (tpos + fneg), copy=False)
- fpr = np.nan_to_num(fpos / (fpos + tneg), copy=False)
+ tpr = np.nan_to_num(tpos / (tpos + fneg))
+ fpr = np.nan_to_num(fpos / (fpos + tneg))
auc = sklearn.metrics.auc(fpr, tpr)
return {
"tpr": tpr,
diff --git a/declearn/model/sklearn/_sgd.py b/declearn/model/sklearn/_sgd.py
index c0c8aba0..c1e0c49a 100644
--- a/declearn/model/sklearn/_sgd.py
+++ b/declearn/model/sklearn/_sgd.py
@@ -445,7 +445,7 @@ class SklearnSGDModel(Model):
raise TypeError(
f"Invalid data type for 'SklearnSGDModel': '{type(array)}'."
)
- return array.astype(self._dtype, copy=False) # type: ignore
+ return array.astype(self._dtype) # type: ignore
def _compute_sample_gradient(
self,
diff --git a/declearn/model/torch/_model.py b/declearn/model/torch/_model.py
index 1ad81596..0fefdcba 100644
--- a/declearn/model/torch/_model.py
+++ b/declearn/model/torch/_model.py
@@ -167,9 +167,9 @@ class TorchModel(Model):
) -> Self:
"""Instantiate a TorchModel from a configuration dict."""
with io.BytesIO(bytes.fromhex(config["model"])) as buffer:
- model = torch.load(buffer)
+ model = torch.load(buffer, weights_only=False)
with io.BytesIO(bytes.fromhex(config["loss"])) as buffer:
- loss = torch.load(buffer)
+ loss = torch.load(buffer, weights_only=False)
if config.get("compile", False) and hasattr(torch, "compile"):
model = torch.compile(model)
return cls(model=model, loss=loss)
--
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